Policymakers in different Countries have introduced different political action to contrast the COVID19 contagion.

1. What are the different containment efforts and is there a strategies resemblance across countries?

2. What is the effect of these policies on the contagion from a global perspective?

3. Has the same action lead to different results in the case of different regions of Italy?

• COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for contagion data

• Oxford COVID-19 Government Response Tracker (OxCGRT) for policies tracking

• World Bank Open Data for population demographic characteristics

• Dimension Reduction via Polychoric PCA for \(11\) ordinal variables (from \(0\) to \(2\) or \(0\) to \(3\)) indicating the stringency level of policies such as

  • School, workplace and transport closing and event cancellation;

  • Gathering, stay-home and internal/international movement restrictions;

  • Information, Testing and Contact Tracing campaigns.

• Functional Data Co-Clustering of the countries aligned to the first contagion (from the 10th day before contagion).

Restriction-based policies on one hand, Tracing and Testing policies on the other hand.

• We analyze the number of Active person, i.e., Confirmed - Deaths - Recovered, \(14\) days after lockdown policies application \(\rightarrow\) Count Dependent Variable (Generalized Poisson Model)

• The data are observed for each country nested within clusters during \(131\) days \(\rightarrow\) Mixed Model

• Confounders from the World Bank Open Data and Oxford Data:
  • FIXED:
    • Population density
  • LONGITUDINAL:
    • Economic: Income Support and Debt/contract relief for households
    • Health: Emergency Investment in healthcare and Investment in vaccines

Effect of the clusters on predicted actives after \(14\) days

Effect of the clusters on predicted actives after \(14\) days considering different levels of policies

Cl1 = KOR SGP; Cl2 = DEU SWE; Cl3 = CAN GRC PRT USA; Cl4 = ESP GBR IRL ITA NLD; Cl5 = AUT BEL CHE DNK FIN FRA NOR

POLICIES:

• Lockdown policies work! respect to impose no measure in general(that’s good!)

• Weak gatherings restrictions still work! For example restrictions on gatherings between 100-1000 people

• Strong Testing and Tracing policies lead to discovering more infected people (luckily!)

COUNTRIES:

• Korea and Singapore are the best countries that acted properly

• Sweden, Germany, Portugal, and Greece better than the other UE countries

• The USA, and Canada better than the other UE countries except for Sweden and Germany

• Italian regions, ethernal divide

• Lockdown almost simultaneous, excepted the Red Zone

• First cases in Lombardia and Lazio hubs

• Policies have no variability between regions

• Baseline control: some regions start from worse situations

• Cannot estimates some effects as for the nations case

• To our defense, integration between databases came lately

• Instrumental variables, more correct but tricky approach

• Phase “1” versus Phase “0” comparison

• Auto-regression: modelling active cases given past numbers
  • Related but not quite to the R0 index

• Random effects for region and date, standard panel approach

• Assuming policies effects seen ~14 days later

• Controlling for testing frequency